Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations83123
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.5 MiB
Average record size in memory486.2 B

Variable types

Categorical6
Numeric17

Alerts

arrival_delay_in_minutes is highly overall correlated with departure_delay_in_minutesHigh correlation
class is highly overall correlated with satisfaction and 1 other fieldsHigh correlation
cleanliness is highly overall correlated with food_and_drink and 2 other fieldsHigh correlation
departure_delay_in_minutes is highly overall correlated with arrival_delay_in_minutesHigh correlation
ease-of_online_booking is highly overall correlated with inflight_wifi_serviceHigh correlation
food_and_drink is highly overall correlated with cleanliness and 2 other fieldsHigh correlation
inflight_entertainment_rating is highly overall correlated with cleanliness and 2 other fieldsHigh correlation
inflight_service is highly overall correlated with on_board_serviceHigh correlation
inflight_wifi_service is highly overall correlated with ease-of_online_booking and 1 other fieldsHigh correlation
on_board_service is highly overall correlated with inflight_serviceHigh correlation
online_boarding is highly overall correlated with satisfactionHigh correlation
satisfaction is highly overall correlated with class and 2 other fieldsHigh correlation
seat_comfort is highly overall correlated with cleanliness and 2 other fieldsHigh correlation
travel_type is highly overall correlated with classHigh correlation
inflight_wifi_service has 2487 (3.0%) zeros Zeros
departure_arrival_time_convenient has 4237 (5.1%) zeros Zeros
ease-of_online_booking has 3604 (4.3%) zeros Zeros
online_boarding has 1959 (2.4%) zeros Zeros
departure_delay_in_minutes has 46977 (56.5%) zeros Zeros
arrival_delay_in_minutes has 46804 (56.3%) zeros Zeros

Reproduction

Analysis started2025-05-22 02:12:18.614352
Analysis finished2025-05-22 02:13:11.160701
Duration52.55 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
Female
42117 
Male
41006 

Length

Max length6
Median length6
Mean length5.0133657
Min length4

Characters and Unicode

Total characters416726
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 42117
50.7%
Male 41006
49.3%

Length

2025-05-22T07:43:11.297795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T07:43:11.434090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 42117
50.7%
male 41006
49.3%

Most occurring characters

ValueCountFrequency (%)
e 125240
30.1%
a 83123
19.9%
l 83123
19.9%
F 42117
 
10.1%
m 42117
 
10.1%
M 41006
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 333603
80.1%
Uppercase Letter 83123
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 125240
37.5%
a 83123
24.9%
l 83123
24.9%
m 42117
 
12.6%
Uppercase Letter
ValueCountFrequency (%)
F 42117
50.7%
M 41006
49.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 416726
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 125240
30.1%
a 83123
19.9%
l 83123
19.9%
F 42117
 
10.1%
m 42117
 
10.1%
M 41006
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 416726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 125240
30.1%
a 83123
19.9%
l 83123
19.9%
F 42117
 
10.1%
m 42117
 
10.1%
M 41006
 
9.8%

customer_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
Loyal Customer
67915 
disloyal Customer
15208 

Length

Max length17
Median length14
Mean length14.548873
Min length14

Characters and Unicode

Total characters1209346
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoyal Customer
2nd rowLoyal Customer
3rd rowdisloyal Customer
4th rowLoyal Customer
5th rowdisloyal Customer

Common Values

ValueCountFrequency (%)
Loyal Customer 67915
81.7%
disloyal Customer 15208
 
18.3%

Length

2025-05-22T07:43:11.593047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T07:43:11.761048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
customer 83123
50.0%
loyal 67915
40.9%
disloyal 15208
 
9.1%

Most occurring characters

ValueCountFrequency (%)
o 166246
13.7%
l 98331
 
8.1%
s 98331
 
8.1%
y 83123
 
6.9%
a 83123
 
6.9%
83123
 
6.9%
C 83123
 
6.9%
u 83123
 
6.9%
t 83123
 
6.9%
m 83123
 
6.9%
Other values (5) 264577
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 975185
80.6%
Uppercase Letter 151038
 
12.5%
Space Separator 83123
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 166246
17.0%
l 98331
10.1%
s 98331
10.1%
y 83123
8.5%
a 83123
8.5%
u 83123
8.5%
t 83123
8.5%
m 83123
8.5%
e 83123
8.5%
r 83123
8.5%
Other values (2) 30416
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
C 83123
55.0%
L 67915
45.0%
Space Separator
ValueCountFrequency (%)
83123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1126223
93.1%
Common 83123
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 166246
14.8%
l 98331
8.7%
s 98331
8.7%
y 83123
7.4%
a 83123
7.4%
C 83123
7.4%
u 83123
7.4%
t 83123
7.4%
m 83123
7.4%
e 83123
7.4%
Other values (4) 181454
16.1%
Common
ValueCountFrequency (%)
83123
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1209346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 166246
13.7%
l 98331
 
8.1%
s 98331
 
8.1%
y 83123
 
6.9%
a 83123
 
6.9%
83123
 
6.9%
C 83123
 
6.9%
u 83123
 
6.9%
t 83123
 
6.9%
m 83123
 
6.9%
Other values (5) 264577
21.9%

age
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.351106
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:11.911638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.099487
Coefficient of variation (CV)0.38371189
Kurtosis-0.7223022
Mean39.351106
Median Absolute Deviation (MAD)12
Skewness-0.006553148
Sum3270982
Variance227.99451
MonotonicityNot monotonic
2025-05-22T07:43:12.118562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 2415
 
2.9%
25 2246
 
2.7%
40 2059
 
2.5%
44 2036
 
2.4%
42 1974
 
2.4%
41 1915
 
2.3%
22 1903
 
2.3%
23 1879
 
2.3%
38 1862
 
2.2%
47 1858
 
2.2%
Other values (65) 62976
75.8%
ValueCountFrequency (%)
7 457
0.5%
8 505
0.6%
9 548
0.7%
10 561
0.7%
11 555
0.7%
12 504
0.6%
13 499
0.6%
14 564
0.7%
15 662
0.8%
16 705
0.8%
ValueCountFrequency (%)
85 13
 
< 0.1%
80 61
 
0.1%
79 33
 
< 0.1%
78 25
 
< 0.1%
77 62
 
0.1%
76 36
 
< 0.1%
75 50
 
0.1%
74 39
 
< 0.1%
73 39
 
< 0.1%
72 164
0.2%

travel_type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
Business travel
57310 
Personal Travel
25813 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters1246845
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Travel
2nd rowBusiness travel
3rd rowBusiness travel
4th rowBusiness travel
5th rowBusiness travel

Common Values

ValueCountFrequency (%)
Business travel 57310
68.9%
Personal Travel 25813
31.1%

Length

2025-05-22T07:43:12.267043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T07:43:12.388083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
travel 83123
50.0%
business 57310
34.5%
personal 25813
 
15.5%

Most occurring characters

ValueCountFrequency (%)
s 197743
15.9%
e 166246
13.3%
r 108936
8.7%
a 108936
8.7%
l 108936
8.7%
n 83123
6.7%
83123
6.7%
v 83123
6.7%
B 57310
 
4.6%
u 57310
 
4.6%
Other values (5) 192059
15.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1054786
84.6%
Uppercase Letter 108936
 
8.7%
Space Separator 83123
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 197743
18.7%
e 166246
15.8%
r 108936
10.3%
a 108936
10.3%
l 108936
10.3%
n 83123
7.9%
v 83123
7.9%
u 57310
 
5.4%
i 57310
 
5.4%
t 57310
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B 57310
52.6%
P 25813
23.7%
T 25813
23.7%
Space Separator
ValueCountFrequency (%)
83123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1163722
93.3%
Common 83123
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 197743
17.0%
e 166246
14.3%
r 108936
9.4%
a 108936
9.4%
l 108936
9.4%
n 83123
7.1%
v 83123
7.1%
B 57310
 
4.9%
u 57310
 
4.9%
i 57310
 
4.9%
Other values (4) 134749
11.6%
Common
ValueCountFrequency (%)
83123
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1246845
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 197743
15.9%
e 166246
13.3%
r 108936
8.7%
a 108936
8.7%
l 108936
8.7%
n 83123
6.7%
83123
6.7%
v 83123
6.7%
B 57310
 
4.6%
u 57310
 
4.6%
Other values (5) 192059
15.4%

class
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
Business
39691 
Eco
37457 
Eco Plus
5975 

Length

Max length8
Median length8
Mean length5.7468932
Min length3

Characters and Unicode

Total characters477699
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEco
2nd rowEco
3rd rowEco
4th rowBusiness
5th rowEco

Common Values

ValueCountFrequency (%)
Business 39691
47.7%
Eco 37457
45.1%
Eco Plus 5975
 
7.2%

Length

2025-05-22T07:43:12.546621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T07:43:12.737323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
eco 43432
48.7%
business 39691
44.5%
plus 5975
 
6.7%

Most occurring characters

ValueCountFrequency (%)
s 125048
26.2%
u 45666
 
9.6%
E 43432
 
9.1%
c 43432
 
9.1%
o 43432
 
9.1%
B 39691
 
8.3%
i 39691
 
8.3%
n 39691
 
8.3%
e 39691
 
8.3%
5975
 
1.3%
Other values (2) 11950
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 382626
80.1%
Uppercase Letter 89098
 
18.7%
Space Separator 5975
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 125048
32.7%
u 45666
 
11.9%
c 43432
 
11.4%
o 43432
 
11.4%
i 39691
 
10.4%
n 39691
 
10.4%
e 39691
 
10.4%
l 5975
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
E 43432
48.7%
B 39691
44.5%
P 5975
 
6.7%
Space Separator
ValueCountFrequency (%)
5975
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 471724
98.7%
Common 5975
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 125048
26.5%
u 45666
 
9.7%
E 43432
 
9.2%
c 43432
 
9.2%
o 43432
 
9.2%
B 39691
 
8.4%
i 39691
 
8.4%
n 39691
 
8.4%
e 39691
 
8.4%
P 5975
 
1.3%
Common
ValueCountFrequency (%)
5975
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 477699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 125048
26.2%
u 45666
 
9.6%
E 43432
 
9.1%
c 43432
 
9.1%
o 43432
 
9.1%
B 39691
 
8.3%
i 39691
 
8.3%
n 39691
 
8.3%
e 39691
 
8.3%
5975
 
1.3%
Other values (2) 11950
 
2.5%

flight_distance
Real number (ℝ)

Distinct3769
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1190.6868
Minimum31
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:13.466219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile175
Q1413
median844
Q31744
95-th percentile3391
Maximum4983
Range4952
Interquartile range (IQR)1331

Descriptive statistics

Standard deviation998.64002
Coefficient of variation (CV)0.83870927
Kurtosis0.26549186
Mean1190.6868
Median Absolute Deviation (MAD)519
Skewness1.1086568
Sum98973455
Variance997281.88
MonotonicityNot monotonic
2025-05-22T07:43:13.627317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337 520
 
0.6%
404 330
 
0.4%
862 307
 
0.4%
594 305
 
0.4%
2475 298
 
0.4%
447 289
 
0.3%
236 281
 
0.3%
192 266
 
0.3%
399 266
 
0.3%
308 261
 
0.3%
Other values (3759) 80000
96.2%
ValueCountFrequency (%)
31 7
 
< 0.1%
56 4
 
< 0.1%
67 103
0.1%
73 50
0.1%
74 24
 
< 0.1%
76 1
 
< 0.1%
77 34
 
< 0.1%
78 27
 
< 0.1%
80 2
 
< 0.1%
82 6
 
< 0.1%
ValueCountFrequency (%)
4983 9
< 0.1%
4963 11
< 0.1%
4817 4
 
< 0.1%
4502 8
< 0.1%
4243 15
< 0.1%
4000 8
< 0.1%
3999 4
 
< 0.1%
3998 7
< 0.1%
3997 6
 
< 0.1%
3996 6
 
< 0.1%

inflight_wifi_service
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.732553
Minimum0
Maximum5
Zeros2487
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:13.786783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3299497
Coefficient of variation (CV)0.48670592
Kurtosis-0.84987479
Mean2.732553
Median Absolute Deviation (MAD)1
Skewness0.038674789
Sum227138
Variance1.7687662
MonotonicityNot monotonic
2025-05-22T07:43:13.897244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 20682
24.9%
2 20578
24.8%
4 15832
19.0%
1 14278
17.2%
5 9266
11.1%
0 2487
 
3.0%
ValueCountFrequency (%)
0 2487
 
3.0%
1 14278
17.2%
2 20578
24.8%
3 20682
24.9%
4 15832
19.0%
5 9266
11.1%
ValueCountFrequency (%)
5 9266
11.1%
4 15832
19.0%
3 20682
24.9%
2 20578
24.8%
1 14278
17.2%
0 2487
 
3.0%

departure_arrival_time_convenient
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0622692
Minimum0
Maximum5
Zeros4237
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:14.007570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5269831
Coefficient of variation (CV)0.49864432
Kurtosis-1.0409263
Mean3.0622692
Median Absolute Deviation (MAD)1
Skewness-0.33549664
Sum254545
Variance2.3316775
MonotonicityNot monotonic
2025-05-22T07:43:14.118809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 20388
24.5%
5 18033
21.7%
3 14356
17.3%
2 13651
16.4%
1 12458
15.0%
0 4237
 
5.1%
ValueCountFrequency (%)
0 4237
 
5.1%
1 12458
15.0%
2 13651
16.4%
3 14356
17.3%
4 20388
24.5%
5 18033
21.7%
ValueCountFrequency (%)
5 18033
21.7%
4 20388
24.5%
3 14356
17.3%
2 13651
16.4%
1 12458
15.0%
0 4237
 
5.1%

ease-of_online_booking
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7563851
Minimum0
Maximum5
Zeros3604
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:14.213823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4010466
Coefficient of variation (CV)0.50829131
Kurtosis-0.91507331
Mean2.7563851
Median Absolute Deviation (MAD)1
Skewness-0.018196255
Sum229119
Variance1.9629316
MonotonicityNot monotonic
2025-05-22T07:43:14.293373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 19510
23.5%
2 19138
23.0%
4 15654
18.8%
1 14097
17.0%
5 11120
13.4%
0 3604
 
4.3%
ValueCountFrequency (%)
0 3604
 
4.3%
1 14097
17.0%
2 19138
23.0%
3 19510
23.5%
4 15654
18.8%
5 11120
13.4%
ValueCountFrequency (%)
5 11120
13.4%
4 15654
18.8%
3 19510
23.5%
2 19138
23.0%
1 14097
17.0%
0 3604
 
4.3%

gate_location
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9780566
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:14.414652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.279064
Coefficient of variation (CV)0.42949619
Kurtosis-1.03266
Mean2.9780566
Median Absolute Deviation (MAD)1
Skewness-0.059396459
Sum247545
Variance1.6360046
MonotonicityNot monotonic
2025-05-22T07:43:14.499438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 22823
27.5%
4 19525
23.5%
2 15534
18.7%
1 14073
16.9%
5 11167
13.4%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 14073
16.9%
2 15534
18.7%
3 22823
27.5%
4 19525
23.5%
5 11167
13.4%
ValueCountFrequency (%)
5 11167
13.4%
4 19525
23.5%
3 22823
27.5%
2 15534
18.7%
1 14073
16.9%
0 1
 
< 0.1%

food_and_drink
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2052861
Minimum0
Maximum5
Zeros88
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:14.580616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3304815
Coefficient of variation (CV)0.41508979
Kurtosis-1.1479414
Mean3.2052861
Median Absolute Deviation (MAD)1
Skewness-0.15268993
Sum266433
Variance1.7701811
MonotonicityNot monotonic
2025-05-22T07:43:14.705395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 19471
23.4%
5 17967
21.6%
3 17738
21.3%
2 17641
21.2%
1 10218
12.3%
0 88
 
0.1%
ValueCountFrequency (%)
0 88
 
0.1%
1 10218
12.3%
2 17641
21.2%
3 17738
21.3%
4 19471
23.4%
5 17967
21.6%
ValueCountFrequency (%)
5 17967
21.6%
4 19471
23.4%
3 17738
21.3%
2 17641
21.2%
1 10218
12.3%
0 88
 
0.1%

online_boarding
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2528422
Minimum0
Maximum5
Zeros1959
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:14.832156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.350905
Coefficient of variation (CV)0.41529988
Kurtosis-0.69885967
Mean3.2528422
Median Absolute Deviation (MAD)1
Skewness-0.4576846
Sum270386
Variance1.8249443
MonotonicityNot monotonic
2025-05-22T07:43:14.965017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 24594
29.6%
3 17445
21.0%
5 16661
20.0%
2 13906
16.7%
1 8558
 
10.3%
0 1959
 
2.4%
ValueCountFrequency (%)
0 1959
 
2.4%
1 8558
 
10.3%
2 13906
16.7%
3 17445
21.0%
4 24594
29.6%
5 16661
20.0%
ValueCountFrequency (%)
5 16661
20.0%
4 24594
29.6%
3 17445
21.0%
2 13906
16.7%
1 8558
 
10.3%
0 1959
 
2.4%

seat_comfort
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4427896
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:15.070216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.318317
Coefficient of variation (CV)0.38292116
Kurtosis-0.92220179
Mean3.4427896
Median Absolute Deviation (MAD)1
Skewness-0.4863397
Sum286175
Variance1.7379597
MonotonicityNot monotonic
2025-05-22T07:43:15.165111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 25507
30.7%
5 21223
25.5%
3 14852
17.9%
2 11936
14.4%
1 9604
 
11.6%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 9604
 
11.6%
2 11936
14.4%
3 14852
17.9%
4 25507
30.7%
5 21223
25.5%
ValueCountFrequency (%)
5 21223
25.5%
4 25507
30.7%
3 14852
17.9%
2 11936
14.4%
1 9604
 
11.6%
0 1
 
< 0.1%

inflight_entertainment_rating
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3629922
Minimum0
Maximum5
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:15.268836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3318798
Coefficient of variation (CV)0.39604012
Kurtosis-1.0594175
Mean3.3629922
Median Absolute Deviation (MAD)1
Skewness-0.36741115
Sum279542
Variance1.7739039
MonotonicityNot monotonic
2025-05-22T07:43:15.419436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 23559
28.3%
5 20277
24.4%
3 15236
18.3%
2 14171
17.0%
1 9871
11.9%
0 9
 
< 0.1%
ValueCountFrequency (%)
0 9
 
< 0.1%
1 9871
11.9%
2 14171
17.0%
3 15236
18.3%
4 23559
28.3%
5 20277
24.4%
ValueCountFrequency (%)
5 20277
24.4%
4 23559
28.3%
3 15236
18.3%
2 14171
17.0%
1 9871
11.9%
0 9
 
< 0.1%

on_board_service
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3836363
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:15.530463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.287778
Coefficient of variation (CV)0.38058997
Kurtosis-0.89290235
Mean3.3836363
Median Absolute Deviation (MAD)1
Skewness-0.41973369
Sum281258
Variance1.6583723
MonotonicityNot monotonic
2025-05-22T07:43:15.661175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 24701
29.7%
5 18944
22.8%
3 18229
21.9%
2 11799
14.2%
1 9449
 
11.4%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 9449
 
11.4%
2 11799
14.2%
3 18229
21.9%
4 24701
29.7%
5 18944
22.8%
ValueCountFrequency (%)
5 18944
22.8%
4 24701
29.7%
3 18229
21.9%
2 11799
14.2%
1 9449
 
11.4%
0 1
 
< 0.1%

leg_room_service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.351407
Minimum0
Maximum5
Zeros371
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:15.773850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3133649
Coefficient of variation (CV)0.39188463
Kurtosis-0.97832768
Mean3.351407
Median Absolute Deviation (MAD)1
Skewness-0.34938333
Sum278579
Variance1.7249273
MonotonicityNot monotonic
2025-05-22T07:43:15.918678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 23094
27.8%
5 19666
23.7%
3 16111
19.4%
2 15659
18.8%
1 8222
 
9.9%
0 371
 
0.4%
ValueCountFrequency (%)
0 371
 
0.4%
1 8222
 
9.9%
2 15659
18.8%
3 16111
19.4%
4 23094
27.8%
5 19666
23.7%
ValueCountFrequency (%)
5 19666
23.7%
4 23094
27.8%
3 16111
19.4%
2 15659
18.8%
1 8222
 
9.9%
0 371
 
0.4%

baggage_handling
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
4
29927 
5
21732 
3
16526 
2
9211 
1
5727 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters83123
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row5
5th row5

Common Values

ValueCountFrequency (%)
4 29927
36.0%
5 21732
26.1%
3 16526
19.9%
2 9211
 
11.1%
1 5727
 
6.9%

Length

2025-05-22T07:43:16.021837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T07:43:16.165199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 29927
36.0%
5 21732
26.1%
3 16526
19.9%
2 9211
 
11.1%
1 5727
 
6.9%

Most occurring characters

ValueCountFrequency (%)
4 29927
36.0%
5 21732
26.1%
3 16526
19.9%
2 9211
 
11.1%
1 5727
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 83123
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 29927
36.0%
5 21732
26.1%
3 16526
19.9%
2 9211
 
11.1%
1 5727
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 83123
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 29927
36.0%
5 21732
26.1%
3 16526
19.9%
2 9211
 
11.1%
1 5727
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 29927
36.0%
5 21732
26.1%
3 16526
19.9%
2 9211
 
11.1%
1 5727
 
6.9%

checkin_service
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3081939
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:16.315461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2647469
Coefficient of variation (CV)0.38230738
Kurtosis-0.82595784
Mean3.3081939
Median Absolute Deviation (MAD)1
Skewness-0.36785912
Sum274987
Variance1.5995848
MonotonicityNot monotonic
2025-05-22T07:43:16.450163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 23297
28.0%
3 22702
27.3%
5 16564
19.9%
2 10314
12.4%
1 10245
12.3%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 10245
12.3%
2 10314
12.4%
3 22702
27.3%
4 23297
28.0%
5 16564
19.9%
ValueCountFrequency (%)
5 16564
19.9%
4 23297
28.0%
3 22702
27.3%
2 10314
12.4%
1 10245
12.3%
0 1
 
< 0.1%

inflight_service
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6438651
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:16.529840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1733505
Coefficient of variation (CV)0.32200713
Kurtosis-0.35240966
Mean3.6438651
Median Absolute Deviation (MAD)1
Skewness-0.6917392
Sum302889
Variance1.3767515
MonotonicityNot monotonic
2025-05-22T07:43:16.641468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 30417
36.6%
5 21733
26.1%
3 16202
19.5%
2 9180
 
11.0%
1 5590
 
6.7%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 5590
 
6.7%
2 9180
 
11.0%
3 16202
19.5%
4 30417
36.6%
5 21733
26.1%
ValueCountFrequency (%)
5 21733
26.1%
4 30417
36.6%
3 16202
19.5%
2 9180
 
11.0%
1 5590
 
6.7%
0 1
 
< 0.1%

cleanliness
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2913514
Minimum0
Maximum5
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:16.756842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3135602
Coefficient of variation (CV)0.39909448
Kurtosis-1.0149664
Mean3.2913514
Median Absolute Deviation (MAD)1
Skewness-0.30292854
Sum273587
Variance1.7254403
MonotonicityNot monotonic
2025-05-22T07:43:16.879390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 21725
26.1%
3 19525
23.5%
5 18330
22.1%
2 12928
15.6%
1 10606
12.8%
0 9
 
< 0.1%
ValueCountFrequency (%)
0 9
 
< 0.1%
1 10606
12.8%
2 12928
15.6%
3 19525
23.5%
4 21725
26.1%
5 18330
22.1%
ValueCountFrequency (%)
5 18330
22.1%
4 21725
26.1%
3 19525
23.5%
2 12928
15.6%
1 10606
12.8%
0 9
 
< 0.1%

departure_delay_in_minutes
Real number (ℝ)

High correlation  Zeros 

Distinct419
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.714556
Minimum0
Maximum1592
Zeros46977
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:17.062603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile77
Maximum1592
Range1592
Interquartile range (IQR)12

Descriptive statistics

Standard deviation38.108874
Coefficient of variation (CV)2.589876
Kurtosis111.02888
Mean14.714556
Median Absolute Deviation (MAD)0
Skewness6.9800633
Sum1223118
Variance1452.2863
MonotonicityNot monotonic
2025-05-22T07:43:17.213202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46977
56.5%
1 2340
 
2.8%
2 1830
 
2.2%
3 1601
 
1.9%
4 1517
 
1.8%
5 1353
 
1.6%
6 1250
 
1.5%
7 1111
 
1.3%
8 1027
 
1.2%
9 1001
 
1.2%
Other values (409) 23116
27.8%
ValueCountFrequency (%)
0 46977
56.5%
1 2340
 
2.8%
2 1830
 
2.2%
3 1601
 
1.9%
4 1517
 
1.8%
5 1353
 
1.6%
6 1250
 
1.5%
7 1111
 
1.3%
8 1027
 
1.2%
9 1001
 
1.2%
ValueCountFrequency (%)
1592 1
< 0.1%
1305 1
< 0.1%
1017 1
< 0.1%
933 1
< 0.1%
930 1
< 0.1%
921 1
< 0.1%
859 1
< 0.1%
853 1
< 0.1%
750 1
< 0.1%
748 1
< 0.1%

arrival_delay_in_minutes
Real number (ℝ)

High correlation  Zeros 

Distinct429
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.017637
Minimum0
Maximum1584
Zeros46804
Zeros (%)56.3%
Negative0
Negative (%)0.0%
Memory size649.5 KiB
2025-05-22T07:43:17.419360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile78
Maximum1584
Range1584
Interquartile range (IQR)13

Descriptive statistics

Standard deviation38.426241
Coefficient of variation (CV)2.5587409
Kurtosis105.39802
Mean15.017637
Median Absolute Deviation (MAD)0
Skewness6.8388741
Sum1248311
Variance1476.576
MonotonicityNot monotonic
2025-05-22T07:43:17.547272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46804
56.3%
1 1765
 
2.1%
2 1640
 
2.0%
3 1552
 
1.9%
4 1550
 
1.9%
5 1323
 
1.6%
6 1276
 
1.5%
7 1198
 
1.4%
8 1147
 
1.4%
9 1008
 
1.2%
Other values (419) 23860
28.7%
ValueCountFrequency (%)
0 46804
56.3%
1 1765
 
2.1%
2 1640
 
2.0%
3 1552
 
1.9%
4 1550
 
1.9%
5 1323
 
1.6%
6 1276
 
1.5%
7 1198
 
1.4%
8 1147
 
1.4%
9 1008
 
1.2%
ValueCountFrequency (%)
1584 1
< 0.1%
1280 1
< 0.1%
1011 1
< 0.1%
952 1
< 0.1%
924 1
< 0.1%
920 1
< 0.1%
860 1
< 0.1%
823 1
< 0.1%
729 1
< 0.1%
720 1
< 0.1%

satisfaction
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
neutral or dissatisfied
47004 
satisfied
36119 

Length

Max length23
Median length23
Mean length16.916654
Min length9

Characters and Unicode

Total characters1406163
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowneutral or dissatisfied
2nd rowneutral or dissatisfied
3rd rowsatisfied
4th rowsatisfied
5th rowsatisfied

Common Values

ValueCountFrequency (%)
neutral or dissatisfied 47004
56.5%
satisfied 36119
43.5%

Length

2025-05-22T07:43:17.674356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T07:43:17.848461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
neutral 47004
26.5%
or 47004
26.5%
dissatisfied 47004
26.5%
satisfied 36119
20.4%

Most occurring characters

ValueCountFrequency (%)
i 213250
15.2%
s 213250
15.2%
e 130127
9.3%
t 130127
9.3%
a 130127
9.3%
d 130127
9.3%
r 94008
6.7%
94008
6.7%
f 83123
 
5.9%
n 47004
 
3.3%
Other values (3) 141012
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1312155
93.3%
Space Separator 94008
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 213250
16.3%
s 213250
16.3%
e 130127
9.9%
t 130127
9.9%
a 130127
9.9%
d 130127
9.9%
r 94008
7.2%
f 83123
 
6.3%
n 47004
 
3.6%
u 47004
 
3.6%
Other values (2) 94008
7.2%
Space Separator
ValueCountFrequency (%)
94008
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1312155
93.3%
Common 94008
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 213250
16.3%
s 213250
16.3%
e 130127
9.9%
t 130127
9.9%
a 130127
9.9%
d 130127
9.9%
r 94008
7.2%
f 83123
 
6.3%
n 47004
 
3.6%
u 47004
 
3.6%
Other values (2) 94008
7.2%
Common
ValueCountFrequency (%)
94008
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1406163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 213250
15.2%
s 213250
15.2%
e 130127
9.3%
t 130127
9.3%
a 130127
9.3%
d 130127
9.3%
r 94008
6.7%
94008
6.7%
f 83123
 
5.9%
n 47004
 
3.3%
Other values (3) 141012
10.0%

Interactions

2025-05-22T07:43:07.239863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:42:26.915050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:29.758931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:32.472811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:35.210061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:37.722912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:40.368315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:43.367769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:45.782264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:48.127853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:50.620833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:53.357915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:56.614794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:43:04.176893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:07.413918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:24.516741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:27.098451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:29.923624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:32.643982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:35.357446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:37.914773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:40.467455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:42:45.945901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:48.265094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:50.728736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:53.593786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:56.763771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:42:32.816450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:42:27.408669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:30.228193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:32.974031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:35.662732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:38.214525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:41.191783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:42:46.700237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:48.940567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:51.571012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:54.360106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:42:30.888102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:33.707991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:36.190017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:42:25.565984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:28.026932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:31.044953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:42:36.378106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:42:42.016295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:44.531892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:46.963490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T07:42:54.697388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:57.937393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:00.340341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:02.903517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:05.552428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:08.834935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:25.745348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:28.189585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:31.175644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:33.972213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:36.526641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:39.089185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:42.165874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:44.630563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:47.062198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:49.466474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:52.045986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:54.843326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:58.045976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:00.490360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:03.000421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:05.739524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:08.949592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:25.882556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:28.379735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:31.358848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:34.108118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:36.725664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:39.258179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:42.291216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:44.767922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:47.181686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:49.607130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:52.227904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:55.505618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:58.192303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:00.620380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:03.134414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:05.914412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:09.099055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:26.050964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:28.560852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:31.510169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:34.243419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:36.828758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:39.423392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:42.435824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:44.917552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:47.314558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:49.807867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:52.375891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:55.647589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:58.339958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:00.736042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:03.290096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:06.064785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:09.246018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:26.248305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:28.680625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:31.662621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:34.394185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:36.947990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:39.588399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:42.552398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:45.062663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:47.430876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:49.913144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:52.546356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:55.805673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:58.458894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:00.892468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:03.402611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:06.232655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:09.415446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:26.384903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:28.812991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:31.815050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:34.509561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:37.107855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:39.792956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:42.694153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:45.216484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:47.557811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:50.051903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:52.742194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:55.973942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:58.606828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:01.054279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:03.550430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:06.450104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:09.550861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:26.499896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:28.964641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:31.942919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:34.725055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:37.208434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:39.958465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:42.852964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:45.367537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:47.699180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:50.180313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:52.887671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:56.103118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:58.711789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:01.237407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:03.718591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:06.641690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:09.750394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:26.613561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:29.473303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:32.107222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:34.842703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:37.337726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:40.059316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:43.002349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:45.475052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:47.847500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:50.333578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:53.014101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:56.268279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:58.855138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:01.402393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:03.863282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:06.849039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:09.881242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:26.773952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:29.630388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:32.290043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:34.990311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:37.559924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:40.237650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:43.186286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:45.604832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:47.976258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:50.489368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:53.180431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:56.448095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:42:59.025861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:01.569535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:04.015399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T07:43:07.069444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2025-05-22T07:43:17.943748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
agearrival_delay_in_minutesbaggage_handlingcheckin_serviceclasscleanlinesscustomer_typedeparture_arrival_time_convenientdeparture_delay_in_minutesease-of_online_bookingflight_distancefood_and_drinkgate_locationgenderinflight_entertainment_ratinginflight_serviceinflight_wifi_serviceleg_room_serviceon_board_serviceonline_boardingsatisfactionseat_comforttravel_type
age1.000-0.0100.0590.0360.2050.0520.3740.036-0.0110.0220.0730.019-0.0020.0130.080-0.0300.0140.0540.0710.2150.2810.1600.341
arrival_delay_in_minutes-0.0101.0000.005-0.0360.000-0.0320.000-0.0090.735-0.015-0.002-0.0330.0050.009-0.044-0.055-0.037-0.018-0.049-0.0530.015-0.0380.004
baggage_handling0.0590.0051.0000.1420.1360.0620.0670.0720.0050.0330.0380.0340.0540.0470.3520.4910.1190.2660.3980.0930.2850.0810.049
checkin_service0.036-0.0360.1421.0000.1250.1730.0280.100-0.0190.0090.0710.081-0.0370.0120.1180.2480.0400.1430.2330.2150.2460.1970.022
class0.2050.0000.1360.1251.0000.1150.1210.1000.0000.1160.3450.0760.1110.0100.1500.1300.1020.1620.1590.2510.5040.1740.554
cleanliness0.052-0.0320.0620.1730.1151.0000.1040.015-0.0170.0150.0820.647-0.0030.0200.6830.1010.1280.0980.1260.3420.3120.6660.094
customer_type0.3740.0000.0670.0280.1210.1041.0000.2910.0000.0550.2510.0790.1250.0310.1190.0550.0380.0730.0760.1910.1860.1720.309
departure_arrival_time_convenient0.036-0.0090.0720.1000.1000.0150.2911.000-0.0050.441-0.0150.0030.4510.007-0.0090.0920.3410.0050.0700.0610.0670.0120.289
departure_delay_in_minutes-0.0110.7350.005-0.0190.000-0.0170.000-0.0051.000-0.0110.025-0.0220.0050.004-0.028-0.032-0.030-0.005-0.027-0.0350.016-0.0210.003
ease-of_online_booking0.022-0.0150.0330.0090.1160.0150.0550.441-0.0111.0000.0670.0310.4610.0050.0410.0350.7130.0920.0360.3690.3180.0270.189
flight_distance0.073-0.0020.0380.0710.3450.0820.251-0.0150.0250.0671.0000.0460.0000.0070.1060.0590.0080.1180.1010.1960.3130.1380.281
food_and_drink0.019-0.0330.0340.0810.0760.6470.0790.003-0.0220.0310.0461.000-0.0010.0100.6120.0430.1310.0320.0580.2400.2230.5580.076
gate_location-0.0020.0050.054-0.0370.111-0.0030.1250.4510.0050.4610.000-0.0011.0000.0060.001-0.0080.334-0.008-0.031-0.0020.1550.0000.155
gender0.0130.0090.0470.0120.0100.0200.0310.0070.0040.0050.0070.0100.0061.0000.0040.0430.0080.0540.0180.0450.0110.0350.008
inflight_entertainment_rating0.080-0.0440.3520.1180.1500.6830.119-0.009-0.0280.0410.1060.6120.0010.0041.0000.4200.1960.3120.4360.3010.4190.6040.164
inflight_service-0.030-0.0550.4910.2480.1300.1010.0550.092-0.0320.0350.0590.043-0.0080.0430.4201.0000.1040.3700.5690.1090.2810.0970.040
inflight_wifi_service0.014-0.0370.1190.0400.1020.1280.0380.341-0.0300.7130.0080.1310.3340.0080.1960.1041.0000.1480.1160.4350.5270.1170.183
leg_room_service0.054-0.0180.2660.1430.1620.0980.0730.005-0.0050.0920.1180.032-0.0080.0540.3120.3700.1481.0000.3620.1380.3420.1180.172
on_board_service0.071-0.0490.3980.2330.1590.1260.0760.070-0.0270.0360.1010.058-0.0310.0180.4360.5690.1160.3621.0000.1770.3320.1460.087
online_boarding0.215-0.0530.0930.2150.2510.3420.1910.061-0.0350.3690.1960.240-0.0020.0450.3010.1090.4350.1380.1771.0000.6190.4380.240
satisfaction0.2810.0150.2850.2460.5040.3120.1860.0670.0160.3180.3130.2230.1550.0110.4190.2810.5270.3420.3320.6191.0000.3850.449
seat_comfort0.160-0.0380.0810.1970.1740.6660.1720.012-0.0210.0270.1380.5580.0000.0350.6040.0970.1170.1180.1460.4380.3851.0000.133
travel_type0.3410.0040.0490.0220.5540.0940.3090.2890.0030.1890.2810.0760.1550.0080.1640.0400.1830.1720.0870.2400.4490.1331.000

Missing values

2025-05-22T07:43:10.158210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-22T07:43:10.752520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

gendercustomer_typeagetravel_typeclassflight_distanceinflight_wifi_servicedeparture_arrival_time_convenientease-of_online_bookinggate_locationfood_and_drinkonline_boardingseat_comfortinflight_entertainment_ratingon_board_serviceleg_room_servicebaggage_handlingcheckin_serviceinflight_servicecleanlinessdeparture_delay_in_minutesarrival_delay_in_minutessatisfaction
0FemaleLoyal Customer30Personal TravelEco1933403505545344500.0neutral or dissatisfied
1FemaleLoyal Customer51Business travelEco925211153222223234729.0neutral or dissatisfied
2Maledisloyal Customer27Business travelEco5050004101155232100.0satisfied
3FemaleLoyal Customer52Business travelBusiness295322223455555455102.0satisfied
4Femaledisloyal Customer15Business travelEco13524442242243535200.0satisfied
5MaleLoyal Customer42Business travelBusiness36485555245444434443.0satisfied
6FemaleLoyal Customer54Business travelBusiness11354155443444444360.0neutral or dissatisfied
7Femaledisloyal Customer24Business travelEco6204043444435312407.0satisfied
8MaleLoyal Customer31Business travelBusiness19513323444443455400.0satisfied
9FemaleLoyal Customer36Business travelBusiness15872222351555535310.0satisfied
gendercustomer_typeagetravel_typeclassflight_distanceinflight_wifi_servicedeparture_arrival_time_convenientease-of_online_bookinggate_locationfood_and_drinkonline_boardingseat_comfortinflight_entertainment_ratingon_board_serviceleg_room_servicebaggage_handlingcheckin_serviceinflight_servicecleanlinessdeparture_delay_in_minutesarrival_delay_in_minutessatisfaction
83113MaleLoyal Customer30Business travelBusiness24023252323311344306.0neutral or dissatisfied
83114MaleLoyal Customer60Business travelBusiness1703222244533335342625.0satisfied
83115FemaleLoyal Customer40Business travelEco1094555444453244435.0satisfied
83116FemaleLoyal Customer33Business travelBusiness3541151345554515200.0satisfied
83117FemaleLoyal Customer44Business travelEco251122224311111112321.0neutral or dissatisfied
83118FemaleLoyal Customer52Business travelBusiness4961111244555555501.0satisfied
83119FemaleLoyal Customer44Personal TravelEco4322424444222252500.0neutral or dissatisfied
83120MaleLoyal Customer46Business travelBusiness34923111343333323300.0neutral or dissatisfied
83121Femaledisloyal Customer37Business travelBusiness3774444242232534200.0neutral or dissatisfied
83122FemaleLoyal Customer23Business travelBusiness2863333344445114544247.0satisfied